At present, upper limb motor rehabilitation relies on specific rehabilitation aids, ignoring the initiative of upper limb motor of patients in the middle and late stages of rehabilitation. This paper proposes a fuzzy evaluation method for active participation based on trajectory error and surface electromyography (sEMG) for patients who gradually have the ability to generate active force. First, the level of motor participation was evaluated using trajectory error signals represented by computer vision. Then, the level of physiological participation was quantified based on muscle activation (MA) characterized by sEMG. Finally, the motor performance and physiological response parameters were input into the fuzzy inference system (FIS). This system was then used to construct the fuzzy decision tree (FDT), which ultimately outputs the active participation level. A controlled experiment of upper limb flexion and extension exercise in 16 healthy subjects demonstrated that the method presented in this paper was effective in quantifying difference in the active participation level of the upper limb in different force-generating states. The calculation results of this method and the active participation assessment method based on sEMG during the task cycle showed that the active participation evaluation values of both methods peaked in the initial cycle: (82.34 ± 9.3) % for this paper's method and (78.44 ± 7.31) % for the sEMG method. In the subsequent cycles, the values of both showed a dynamic change trend of rising first and then falling. Trend consistency verifies the effectiveness of the active participation assessment strategy in this paper, providing a new idea for quantifying the participation level of patients in middle and late stages of upper limb rehabilitation without special equipment mediation.
Humans ; Electromyography/methods* ; Upper Extremity/physiology* ; Fuzzy Logic ; Exercise Therapy/methods* ; Muscle, Skeletal/physiology* ; Male

In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processing ability of electrocardiogram (ECG) signals has been greatly enhanced through new computer technologies, enabling the auxiliary diagnosis technology for cardiovascular disease (CVD) to achieve new improvements. This article discusses the application of machine learning in ECG processing, especially in the auxiliary diagnosis of diseases. Firstly, the conventional signal preprocessing methods are introduced, and then the EEG signal processing methods based on feature extraction and fuzzy classification are explored. Secondly, the application of auxiliary diagnosis in CVD is further summarized. Finally, the advantages and disadvantages of the two methods are analyzed, and based on this, a design of an auxiliary diagnostic system compatible with the two methods is proposed, providing a new perspective for similar applied researches in the future.
Machine Learning ; Cardiovascular Diseases/diagnosis* ; Humans ; Electrocardiography ; Signal Processing, Computer-Assisted ; Diagnosis, Computer-Assisted ; Fuzzy Logic ; Electroencephalography

OBJECTIVE: To enhance the reliability of medical equipment, this study aims to develop a failure cause diagnosis model and provide rational suggestions for efficient equipment use. METHODS: Combine fault tree analysis (FTA) to identify basic events causing equipment failure and calculate their prior probabilities. Obtain conditional probability tables for each node through expert assessment. Integrate triangular fuzzy number theory with Bayesian network (BN) to construct a fuzzy Bayesian network (FBN) for posterior probability inference and sensitivity analysis. RESULTS: Using endoscopes as the subject, the analysis shows that the model accurately calculates the endoscope failure probability at 0.385%, and identifies the key causes: improper cleaning ( X5, posterior probability 0.36064), untimely fault detection ( X8, posterior probability 0.23571), irregular transportation ( X6, posterior probability 0.11344), and natural aging ( X10, posterior probability 0.11377). Sensitivity analysis also confirms their influence weights (mutual information values are 0.00749, 0.00591, 0.00202, 0.00174). CONCLUSION The model can accurately perform quantitative analysis and rapid fault location of medical equipment failures, enabling effective preventive measures.
Bayes Theorem ; Fuzzy Logic ; Equipment Failure Analysis/methods* ; Equipment Failure ; Algorithms

The proton treatment control system is the supporting software of the proton therapy device, which specifically coordinates and controls the status and work of each subsystem. In this study, the software architecture and hardware implementation of the proton treatment control system was developed and built a foundation for the overall debugging. Using C# programming language and WPF programming techniques, TCP network communication protocol specified by the proton treatment technical document and MVVM pattern in Windows system, the logic design and implementation of each level were studied. Meanwhile, the communication interface between the subsystems under TCP communication protocol was agreed. The logic design and research of the setup field and treatment field were carried out. And the User Interface was designed and developed using the above technology. The program realizes the communication and interaction between the proton treatment control system and each subsystem, so as to control and monitor the whole treatment process. The proton treatment control system provides a software basis for the remote overall debugging and on-line monitor and control of proton treatment device.
Protons ; User-Computer Interface ; Software ; Computers ; Logic

OBJECTIVE: This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery. METHODS: A method for classification of surgical risks was developed. The effect of rotation of the current-voltage characteristics at biologically active points (acupuncture points) was used for the formation of classifier descriptors. The effect determined reversible and non-reversible changes in electrical resistance at acupuncture points with periodic exposure to a sawtooth probe current. Then, the developed method was tested on the prediction of the success of surgical treatment of benign prostatic hyperplasia. RESULTS: Input descriptors were obtained from collected data including current-voltage characteristics of 5 acupuncture points and composed of 27 arrays feeding in the model. The maximum diagnostic sensitivity of the classifier for the success of a surgical operation in the control sample was 88% and for testing data set prediction accuracy was 97%. CONCLUSION The use of tuples of current-voltage characteristic descriptors of acupuncture points in the classifiers could be used to predict the success of surgical treatment with satisfactory accuracy. The model can be a valuable tool to support physicians' diagnosis.
Acupuncture Points ; Acupuncture Therapy ; Fuzzy Logic

Risks exist in medicine related fields, which cannot be defined and quantified precisely. It is necessary to adopt a method for the risk assessment of uncertain and fuzzy phenomenon. This paper summarizes the thinking, procedure, advantage and application of fuzzy analytic hierarchy process in the risk assessment in medicine related fields for the purpose of providing reference for its further application.
Analytic Hierarchy Process ; Fuzzy Logic ; Humans ; Risk Assessment/methods*

This paper explores the statistical distribution characteristics of coating film thickness, so as to present a new method for determining coating endpoint based on 3σ criterion and logic regression. Firstly, the spectrum and thickness of 4 batch samples were collected. Secondly, the spectral range of normal products was obtained by 3σ criterion, with the spectral feature NI as the number of test spectrum in the above range. Then, the model based on 3σ criterion and logic regression was built according to the best condition in K-fold cross-validation and the determined threshold of qualified rate in the coating endpoint. Finally, the qualified rate of test set samples at different time points was calculated by the above model, and the above change trend and the threshold value were combined to determine the coating endpoint. The results of KS analysis showed the distribution of thickness of the qualified products followed the normal distribution(P=0.081>0.05). The accuracy of the coating endpoint determination was as high as 100% by the model based on 3σ criterion and logic regression when the determined threshold of qualified rate was 90%. Therefore, the 3σ criterion was feasible to the research of coating eligibility. This paper reveals certain random phenomena in the coating process, and the method features a high accuracy, quick analysis and a good interpretability, which provides a reference for online detection and qualify evaluation in future.
Endpoint Determination ; Logic ; Research Design ; Tablets

Adult ; Colon ; Colon, Transverse ; Duodenum ; Fetus ; Gestational Age ; Humans ; Ileum ; Jejunum ; Logic ; Mesocolon ; Omentum ; Peritoneum ; Pyloric Antrum ; Pylorus ; Stomach ; Viscera

We constructed the family tree database (DB) by using a new family code system that can logically express interpersonal family relationships and by comparing and complementing health insurance eligibility data and resident register data of the National Health Information Database (NHID). In the family tree DB, Parents and grandparents are matched for more than 95% of those who were born between 2010 and 2017. Codes for inverse relationships and extended relationships are generated using sequences of the three-digit basic family codes. The family tree DB contains variables such as sex, birth year, family relations, and degree of kinship (maximum of 4) between subjects and family members. Using the family tree DB, we find that prevalence rates of hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and cancer are higher for those with family history. The family tree DB may omit some relationships due to incomplete past data, and some family relations cannot be uniquely determined because the source data only contain relationships between head and members of the household. The family tree DB is a part of the NHID, and researchers can submit requests for data on the website at http://nhiss.nhis.or.kr. Requested data will be provided after approval from the data service review board. However, the family tree DB can be limitedly provided for studies with high public value in order to maximize personal information protection.
Cerebrovascular Disorders ; Complement System Proteins ; Computer Security ; Family Characteristics ; Family Relations ; Grandparents ; Head ; Humans ; Hypertension ; Insurance, Health ; Interpersonal Relations ; Korea ; Logic ; Myocardial Ischemia ; Parents ; Parturition ; Pedigree ; Prevalence

OBJECTIVE: The purpose of this study is to identify the demographic variables that are affecting performances on the Logical Memory (LM) subtest included in the Korean version of the Wechsler Memory Scale (WMS)-IV and to provide normative data on the LM subtest for the middle-age and elderly Korean people.METHODS: The participants were 435 non-demented adults aging from 50 to 90 and with the educational level ranging from 0 to 21 years.RESULTS: Age and education were found to be significantly associated with performance on the LM subtest, while gender effect was not statistically significant. Therefore, we stratified the norm blocks by age and education. Age was divided into three groups: 50–59, 60–74, and 75–90 years. Education was stratified into three groups: 0–8 years, 9–12 years, and 13 years or more.CONCLUSION: The normative data provided in the current study are expected to be useful in clinical and research settings to detect or define subtle changes in episodic memory in Korean adults and elderly, and can also be used for cross-cultural comparison of verbal episodic memory performance among elderly populations using different languages.
Adult ; Aged ; Aging ; Cross-Cultural Comparison ; Education ; Humans ; Logic ; Memory ; Memory, Episodic